{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "year=2019\n",
    "month=7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import sys\n",
    "sys.path.append('../../py')\n",
    "import db\n",
    "import weighted\n",
    "import inspect\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy.stats as stats\n",
    "import numpy as np\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_original=pd.read_sql(sql=f\"select * from _{year}{month:02} where monthly_salary>0 and monthly_salary<80000\", con=db.get_conn())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(94570, 94)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_original.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data_original"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "del data['publish_date']\n",
    "del data['published_on_weekend']\n",
    "del data['title']\n",
    "del data['company_title']\n",
    "del data['company_description']\n",
    "del data['job_description']\n",
    "del data['job_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_sub_stats_by_prefix(data, prefix):\n",
    "    \n",
    "    features = [feature for feature in data.columns if feature.startswith(prefix)]\n",
    "    salary_mean=[]\n",
    "    salary_median=[]\n",
    "    salary_95_min=[]\n",
    "    salary_95_max=[]\n",
    "    count=[]\n",
    "    \n",
    "    features_out=[]\n",
    "    for feature in features:\n",
    "        #print(feature)\n",
    "        idata=data[data[feature]==1]\n",
    "        headcount=idata.headcount.sum()\n",
    "        values = idata.monthly_salary.values\n",
    "        weights = idata.headcount.values\n",
    "        #print(str(headcount))\n",
    "        if headcount==0:\n",
    "            continue\n",
    "        \n",
    "        salary_mean.append(weighted.weighted_mean(values, weights))\n",
    "        q = weighted.weighted_quantile(values,[0.025,0.5,0.975],weights)\n",
    "        salary_median.append(q[1])\n",
    "        salary_95_min.append(q[0])\n",
    "        salary_95_max.append(q[2])\n",
    "        count.append(idata.headcount.sum())\n",
    "        features_out.append(feature)\n",
    "    sub_data=pd.DataFrame()\n",
    "    sub_data['rank']=range(0,len(features_out))\n",
    "    sub_data[prefix]=[f.replace(prefix,'') for f in features_out]\n",
    "    sub_data['salary_mean']=salary_mean\n",
    "    sub_data['salary_median']=salary_median\n",
    "    sub_data['salary_95_min']=salary_95_min\n",
    "    sub_data['salary_95_max']=salary_95_max\n",
    "    sub_data['head_count']=count\n",
    "    sub_data['percentage']=count/np.sum(count)\n",
    "    sub_data=sub_data.sort_values(by='salary_mean', ascending=False)\n",
    "    sub_data['rank']=range(1,len(features_out)+1)\n",
    "    #sub_data=sub_data.reset_index()\n",
    "    return sub_data\n",
    "\n",
    "def apply_style(sub_data):\n",
    "    return sub_data.style.hide_index().format(\n",
    "        {\"percentage\":\"{:.2%}\",\"salary_mean\":\"{:.0f}\",\"salary_median\":\"{:.0f}\",\"salary_95_min\":\"{:.0f}\",\"salary_95_max\":\"{:.0f}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_pl=get_sub_stats_by_prefix(data,'pl_')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_82f316cc_bab5_11e9_91fd_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >salary_mean</th> \n",
       "        <th class=\"col_heading level0 col3\" >salary_median</th> \n",
       "        <th class=\"col_heading level0 col4\" >salary_95_min</th> \n",
       "        <th class=\"col_heading level0 col5\" >salary_95_max</th> \n",
       "        <th class=\"col_heading level0 col6\" >head_count</th> \n",
       "        <th class=\"col_heading level0 col7\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col1\" class=\"data row0 col1\" >haskell</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col2\" class=\"data row0 col2\" >24352</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col3\" class=\"data row0 col3\" >22222</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col4\" class=\"data row0 col4\" >8375</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col5\" class=\"data row0 col5\" >45000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col6\" class=\"data row0 col6\" >54</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow0_col7\" class=\"data row0 col7\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col1\" class=\"data row1 col1\" >julia</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col2\" class=\"data row1 col2\" >21500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col3\" class=\"data row1 col3\" >21500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col4\" class=\"data row1 col4\" >21500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col5\" class=\"data row1 col5\" >21500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col6\" class=\"data row1 col6\" >2</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow1_col7\" class=\"data row1 col7\" >0.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col1\" class=\"data row2 col1\" >rust</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col2\" class=\"data row2 col2\" >20022</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col3\" class=\"data row2 col3\" >17500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col4\" class=\"data row2 col4\" >5550</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col5\" class=\"data row2 col5\" >56214</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col6\" class=\"data row2 col6\" >326</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow2_col7\" class=\"data row2 col7\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col1\" class=\"data row3 col1\" >python</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col2\" class=\"data row3 col2\" >17979</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col3\" class=\"data row3 col3\" >15833</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col4\" class=\"data row3 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col5\" class=\"data row3 col5\" >45000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col6\" class=\"data row3 col6\" >31776</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow3_col7\" class=\"data row3 col7\" >7.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col1\" class=\"data row4 col1\" >go</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col2\" class=\"data row4 col2\" >17647</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col3\" class=\"data row4 col3\" >15000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col4\" class=\"data row4 col4\" >5500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col5\" class=\"data row4 col5\" >40000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col6\" class=\"data row4 col6\" >29925</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow4_col7\" class=\"data row4 col7\" >7.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col1\" class=\"data row5 col1\" >matlab</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col2\" class=\"data row5 col2\" >17561</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col3\" class=\"data row5 col3\" >17000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col4\" class=\"data row5 col4\" >5024</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col5\" class=\"data row5 col5\" >37500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col6\" class=\"data row5 col6\" >5839</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow5_col7\" class=\"data row5 col7\" >1.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col1\" class=\"data row6 col1\" >perl</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col2\" class=\"data row6 col2\" >17446</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col3\" class=\"data row6 col3\" >17500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col4\" class=\"data row6 col4\" >5250</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col5\" class=\"data row6 col5\" >40097</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col6\" class=\"data row6 col6\" >3173</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow6_col7\" class=\"data row6 col7\" >0.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col1\" class=\"data row7 col1\" >lua</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col2\" class=\"data row7 col2\" >17086</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col3\" class=\"data row7 col3\" >15000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col4\" class=\"data row7 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col5\" class=\"data row7 col5\" >43594</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col6\" class=\"data row7 col6\" >3105</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow7_col7\" class=\"data row7 col7\" >0.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col1\" class=\"data row8 col1\" >ruby</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col2\" class=\"data row8 col2\" >16520</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col3\" class=\"data row8 col3\" >15500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col4\" class=\"data row8 col4\" >2507</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col5\" class=\"data row8 col5\" >35000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col6\" class=\"data row8 col6\" >1183</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow8_col7\" class=\"data row8 col7\" >0.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col1\" class=\"data row9 col1\" >kotlin</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col2\" class=\"data row9 col2\" >15477</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col3\" class=\"data row9 col3\" >12500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col4\" class=\"data row9 col4\" >5782</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col5\" class=\"data row9 col5\" >35000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col6\" class=\"data row9 col6\" >1046</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow9_col7\" class=\"data row9 col7\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col1\" class=\"data row10 col1\" >cpp</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col2\" class=\"data row10 col2\" >15460</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col3\" class=\"data row10 col3\" >13000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col4\" class=\"data row10 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col5\" class=\"data row10 col5\" >37500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col6\" class=\"data row10 col6\" >66263</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow10_col7\" class=\"data row10 col7\" >16.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col1\" class=\"data row11 col1\" >swift</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col2\" class=\"data row11 col2\" >14492</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col3\" class=\"data row11 col3\" >12500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col4\" class=\"data row11 col4\" >5000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col5\" class=\"data row11 col5\" >35000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col6\" class=\"data row11 col6\" >3155</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow11_col7\" class=\"data row11 col7\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col1\" class=\"data row12 col1\" >objective_c</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col2\" class=\"data row12 col2\" >13874</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col3\" class=\"data row12 col3\" >12500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col4\" class=\"data row12 col4\" >6393</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col5\" class=\"data row12 col5\" >35000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col6\" class=\"data row12 col6\" >408</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow12_col7\" class=\"data row12 col7\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col2\" class=\"data row13 col2\" >13770</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col3\" class=\"data row13 col3\" >12500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col4\" class=\"data row13 col4\" >5894</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col5\" class=\"data row13 col5\" >29988</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col6\" class=\"data row13 col6\" >1062</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow13_col7\" class=\"data row13 col7\" >0.26%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col1\" class=\"data row14 col1\" >java</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col2\" class=\"data row14 col2\" >13392</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col3\" class=\"data row14 col3\" >12500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col4\" class=\"data row14 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col5\" class=\"data row14 col5\" >32500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col6\" class=\"data row14 col6\" >139298</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow14_col7\" class=\"data row14 col7\" >33.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col1\" class=\"data row15 col1\" >php</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col2\" class=\"data row15 col2\" >13055</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col3\" class=\"data row15 col3\" >11500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col4\" class=\"data row15 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col5\" class=\"data row15 col5\" >36163</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col6\" class=\"data row15 col6\" >19577</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow15_col7\" class=\"data row15 col7\" >4.73%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col1\" class=\"data row16 col1\" >javascript</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col2\" class=\"data row16 col2\" >11542</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col3\" class=\"data row16 col3\" >11000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col4\" class=\"data row16 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col5\" class=\"data row16 col5\" >24000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col6\" class=\"data row16 col6\" >53425</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow16_col7\" class=\"data row16 col7\" >12.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col1\" class=\"data row17 col1\" >c_sharp</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col2\" class=\"data row17 col2\" >11201</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col3\" class=\"data row17 col3\" >10000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col4\" class=\"data row17 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col5\" class=\"data row17 col5\" >25000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col6\" class=\"data row17 col6\" >52447</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow17_col7\" class=\"data row17 col7\" >12.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col1\" class=\"data row18 col1\" >vba</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col2\" class=\"data row18 col2\" >10875</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col3\" class=\"data row18 col3\" >10000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col4\" class=\"data row18 col4\" >3478</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col5\" class=\"data row18 col5\" >25000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col6\" class=\"data row18 col6\" >462</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow18_col7\" class=\"data row18 col7\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col1\" class=\"data row19 col1\" >delphi</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col2\" class=\"data row19 col2\" >10858</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col3\" class=\"data row19 col3\" >10000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col4\" class=\"data row19 col4\" >3750</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col5\" class=\"data row19 col5\" >22789</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col6\" class=\"data row19 col6\" >1459</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow19_col7\" class=\"data row19 col7\" >0.35%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col1\" class=\"data row20 col1\" >visual_basic</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col2\" class=\"data row20 col2\" >9633</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col3\" class=\"data row20 col3\" >9000</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col4\" class=\"data row20 col4\" >4500</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col5\" class=\"data row20 col5\" >18833</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col6\" class=\"data row20 col6\" >74</td> \n",
       "        <td id=\"T_82f316cc_bab5_11e9_91fd_701ce71031efrow20_col7\" class=\"data row20 col7\" >0.02%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x17974bf30f0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "apply_style(data_pl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style  type=\"text/css\" >\n",
       "</style>  \n",
       "<table id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031ef\" > \n",
       "<thead>    <tr> \n",
       "        <th class=\"col_heading level0 col0\" >rank</th> \n",
       "        <th class=\"col_heading level0 col1\" >pl_</th> \n",
       "        <th class=\"col_heading level0 col2\" >percentage</th> \n",
       "    </tr></thead> \n",
       "<tbody>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow0_col0\" class=\"data row0 col0\" >1</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow0_col1\" class=\"data row0 col1\" >java</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow0_col2\" class=\"data row0 col2\" >33.64%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow1_col0\" class=\"data row1 col0\" >2</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow1_col1\" class=\"data row1 col1\" >cpp</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow1_col2\" class=\"data row1 col2\" >16.00%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow2_col0\" class=\"data row2 col0\" >3</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow2_col1\" class=\"data row2 col1\" >javascript</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow2_col2\" class=\"data row2 col2\" >12.90%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow3_col0\" class=\"data row3 col0\" >4</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow3_col1\" class=\"data row3 col1\" >c_sharp</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow3_col2\" class=\"data row3 col2\" >12.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow4_col0\" class=\"data row4 col0\" >5</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow4_col1\" class=\"data row4 col1\" >python</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow4_col2\" class=\"data row4 col2\" >7.67%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow5_col0\" class=\"data row5 col0\" >6</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow5_col1\" class=\"data row5 col1\" >go</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow5_col2\" class=\"data row5 col2\" >7.23%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow6_col0\" class=\"data row6 col0\" >7</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow6_col1\" class=\"data row6 col1\" >php</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow6_col2\" class=\"data row6 col2\" >4.73%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow7_col0\" class=\"data row7 col0\" >8</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow7_col1\" class=\"data row7 col1\" >matlab</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow7_col2\" class=\"data row7 col2\" >1.41%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow8_col0\" class=\"data row8 col0\" >9</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow8_col1\" class=\"data row8 col1\" >perl</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow8_col2\" class=\"data row8 col2\" >0.77%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow9_col0\" class=\"data row9 col0\" >10</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow9_col1\" class=\"data row9 col1\" >swift</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow9_col2\" class=\"data row9 col2\" >0.76%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow10_col0\" class=\"data row10 col0\" >11</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow10_col1\" class=\"data row10 col1\" >lua</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow10_col2\" class=\"data row10 col2\" >0.75%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow11_col0\" class=\"data row11 col0\" >12</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow11_col1\" class=\"data row11 col1\" >delphi</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow11_col2\" class=\"data row11 col2\" >0.35%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow12_col0\" class=\"data row12 col0\" >13</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow12_col1\" class=\"data row12 col1\" >ruby</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow12_col2\" class=\"data row12 col2\" >0.29%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow13_col0\" class=\"data row13 col0\" >14</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow13_col1\" class=\"data row13 col1\" >typescript</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow13_col2\" class=\"data row13 col2\" >0.26%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow14_col0\" class=\"data row14 col0\" >15</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow14_col1\" class=\"data row14 col1\" >kotlin</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow14_col2\" class=\"data row14 col2\" >0.25%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow15_col0\" class=\"data row15 col0\" >16</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow15_col1\" class=\"data row15 col1\" >vba</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow15_col2\" class=\"data row15 col2\" >0.11%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow16_col0\" class=\"data row16 col0\" >17</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow16_col1\" class=\"data row16 col1\" >objective_c</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow16_col2\" class=\"data row16 col2\" >0.10%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow17_col0\" class=\"data row17 col0\" >18</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow17_col1\" class=\"data row17 col1\" >rust</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow17_col2\" class=\"data row17 col2\" >0.08%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow18_col0\" class=\"data row18 col0\" >19</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow18_col1\" class=\"data row18 col1\" >visual_basic</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow18_col2\" class=\"data row18 col2\" >0.02%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow19_col0\" class=\"data row19 col0\" >20</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow19_col1\" class=\"data row19 col1\" >haskell</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow19_col2\" class=\"data row19 col2\" >0.01%</td> \n",
       "    </tr>    <tr> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow20_col0\" class=\"data row20 col0\" >21</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow20_col1\" class=\"data row20 col1\" >julia</td> \n",
       "        <td id=\"T_82f587c8_bab5_11e9_ab8d_701ce71031efrow20_col2\" class=\"data row20 col2\" >0.00%</td> \n",
       "    </tr></tbody> \n",
       "</table> "
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x1797a7489e8>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_pl=data_pl[['pl_','percentage']].sort_values(by='percentage', ascending=False)\n",
    "data_pl['rank']=range(1,22)\n",
    "data_pl=data_pl[['rank','pl_','percentage']]\n",
    "#data_pl.style.format({\"percentage\":\"{:.2%}\"})\n",
    "data_pl.style.hide_index().format({\"percentage\":\"{:.2%}\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data_pl=data_pl.sort_values(by='percentage')\n",
    "\n",
    "# Pie chart, where the slices will be ordered and plotted counter-clockwise:\n",
    "labels = data_pl['pl_']\n",
    "sizes = data_pl['percentage']\n",
    "#explode = (0, 0.1, 0, 0)  # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
    "\n",
    "fig1, ax1 = plt.subplots(figsize=(15, 9))\n",
    "ax1.pie(sizes, labels=labels, autopct='%1.1f%%',\n",
    "        shadow=True, startangle=90)\n",
    "ax1.axis('equal')  # Equal aspect ratio ensures that pie is drawn as a circle.\n",
    "plt.title(\"Programming Languages in China, March 2019\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Word Cloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=WordCloud()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc_dict = {}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index, row in data_pl.iterrows():\n",
    "    wc_dict[row['pl_']]=row['percentage']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "wc=wc.generate_from_frequencies(wc_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.imshow(wc, interpolation=\"bilinear\")\n",
    "plt.axis(\"off\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
